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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM29415

Face Corrupt Identification Using LSTM And ResNext

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM29415

Volume:

8 |

3 |

Mar 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM29415

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

TDK

T. Devanshu Kumar

PA

P. Ananditha

KSS

K. Sai Srikari

GA

G. Anirvaj

VGS

V. Geethika Siri

GS

G. Sateesh

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Abstract

People will believe in what they see rather than hidden facts. In recent times as technology is improving and it became very easy to change a particular person in an existing image or video with someone else. So, spreading these types of modified videos causes spamming and peculating wrong information over social media and this will cause the great extent of misleading and threatening to the common people. To overcome these tedious situations, we are building a deep learning model that will identify the genuinity of the video or image in terms of the origin of its source. In our proposed system we are using Long Short-Term Memory and ResNext.

KEYWORDS

ResNext Convolution neural network Recurrent Neural Network (RNN) Long Short- Term Memory (LSTM) Computer vision Deepfake Video Detection Pre Processing Deep Learning Image Processing Classification.

Cite This Article

T. Devanshu Kumar, P. Ananditha, K. Sai Srikari, G. Anirvaj, V. Geethika Siri, G. Sateesh, "Face Corrupt Identification Using LSTM And ResNext," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 3, Mar 2024. https://doi.org/10.55041/IJSREM29415

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Publication Date

12/31/45368

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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